def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
parser.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
parser.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
parser.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Binary mask of voxels to include in TE '
'Dependent ANAlysis. Must be in the same '
'space as `data`.'),
default=None)
parser.add_argument('--fitmode',
dest='fitmode',
action='store',
choices=['all', 'ts'],
help=('Monoexponential model fitting scheme. '
'"all" means that the model is fit, per voxel, '
'across all timepoints. '
'"ts" means that the model is fit, per voxel '
'and per timepoint.'),
default='all')
parser.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s', 'ste'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default), ste (Poser)'),
default='t2s')
parser.add_argument('--label',
dest='label',
type=str,
help='Label for output directory.',
default=None)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group('Required Arguments')
required.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
required.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
optional.add_argument('--out-dir',
dest='out_dir',
type=str,
metavar='PATH',
help='Output directory.',
default='.')
optional.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Binary mask of voxels to include in TE '
'Dependent ANAlysis. Must be in the same '
'space as `data`.'),
default=None)
optional.add_argument('--fittype',
dest='fittype',
action='store',
choices=['loglin', 'curvefit'],
help='Desired Fitting Method'
'"loglin" means that a linear model is fit'
' to the log of the data, default'
'"curvefit" means that a more computationally'
'demanding monoexponential model is fit'
'to the raw data',
default='loglin')
optional.add_argument(
'--fitmode',
dest='fitmode',
action='store',
choices=['all', 'ts'],
help=('Monoexponential model fitting scheme. '
'"all" means that the model is fit, per voxel, '
'across all timepoints. '
'"ts" means that the model is fit, per voxel '
'and per timepoint.'),
default='all')
optional.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s', 'paid'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default), paid (Poser)'),
default='t2s')
optional.add_argument('--n-threads',
dest='n_threads',
type=int,
action='store',
help=('Number of threads to use. Used by '
'threadpoolctl to set the parameter outside '
'of the workflow function. Higher numbers of '
'threads tend to slow down performance on '
'typical datasets. Default is 1.'),
default=1)
optional.add_argument('--debug',
dest='debug',
help=argparse.SUPPRESS,
action='store_true',
default=False)
optional.add_argument('--quiet',
dest='quiet',
help=argparse.SUPPRESS,
action='store_true',
default=False)
parser._action_groups.append(optional)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
required.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
required.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
optional.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=("Binary mask of voxels to include in TE "
"Dependent ANAlysis. Must be in the same "
"space as `data`. If an explicit mask is not "
"provided, then Nilearn's compute_epi_mask "
"function will be used to derive a mask "
"from the first echo's data."),
default=None)
optional.add_argument('--mix',
dest='mixm',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing mixing matrix. If not '
'provided, ME-PCA & ME-ICA is done.'),
default=None)
optional.add_argument('--ctab',
dest='ctab',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing a component table from which '
'to extract pre-computed classifications.'),
default=None)
optional.add_argument('--manacc',
dest='manacc',
help=('Comma separated list of manually '
'accepted components'),
default=None)
optional.add_argument('--sourceTEs',
dest='source_tes',
type=str,
help=('Source TEs for models. E.g., 0 for all, '
'-1 for opt. com., and 1,2 for just TEs 1 and '
'2. Default=-1.'),
default=-1)
optional.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default)'),
default='t2s')
optional.add_argument('--verbose',
dest='verbose',
action='store_true',
help='Generate intermediate and additional files.',
default=False)
optional.add_argument('--tedort',
dest='tedort',
action='store_true',
help=('Orthogonalize rejected components w.r.t. '
'accepted components prior to denoising.'),
default=False)
optional.add_argument('--gscontrol',
dest='gscontrol',
required=False,
action='store',
nargs='+',
help=('Perform additional denoising to remove '
'spatially diffuse noise. Default is None. '
'This argument can be single value or a space '
'delimited list'),
choices=['t1c', 'gsr'],
default=None)
optional.add_argument('--tedpca',
dest='tedpca',
help='Method with which to select components in TEDPCA',
choices=['mle', 'kundu', 'kundu-stabilize'],
default='mle')
optional.add_argument('--out-dir',
dest='out_dir',
type=str,
help='Output directory.',
default='.')
optional.add_argument('--seed',
dest='fixed_seed',
type=int,
help=('Value passed to repr(mdp.numx_rand.seed()). '
'Set to an integer value for reproducible ICA results. '
'Set to -1 for varying results across ICA calls. '
'Default=42.'),
default=42)
optional.add_argument('--png',
dest='png',
action='store_true',
help=('Creates a figures folder with static component '
'maps, timecourse plots and other diagnostic '
'images'),
default=False)
optional.add_argument('--png-cmap',
dest='png_cmap',
type=str,
help=('Colormap for figures'),
default='coolwarm')
optional.add_argument('--maxit',
dest='maxit',
type=int,
help=('Maximum number of iterations for ICA.'),
default=500)
optional.add_argument('--maxrestart',
dest='maxrestart',
type=int,
help=('Maximum number of attempts for ICA. If ICA '
'fails to converge, the fixed seed will be '
'updated and ICA will be run again. If '
'convergence is achieved before maxrestart '
'attempts, ICA will finish early.'),
default=10)
optional.add_argument('--debug',
dest='debug',
help=argparse.SUPPRESS,
action='store_true',
default=False)
optional.add_argument('--quiet',
dest='quiet',
help=argparse.SUPPRESS,
action='store_true',
default=False)
parser._action_groups.append(optional)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group("Required Arguments")
required.add_argument(
"-d",
dest="data",
nargs="+",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=("Multi-echo dataset for analysis. May be a "
"single file with spatially concatenated data "
"or a set of echo-specific files, in the same "
"order as the TEs are listed in the -e "
"argument."),
required=True,
)
required.add_argument(
"-e",
dest="tes",
nargs="+",
metavar="TE",
type=float,
help="Echo times (in ms). E.g., 15.0 39.0 63.0",
required=True,
)
optional.add_argument(
"--out-dir",
dest="out_dir",
type=str,
metavar="PATH",
help="Output directory.",
default=".",
)
optional.add_argument(
"--mask",
dest="mask",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=("Binary mask of voxels to include in TE "
"Dependent ANAlysis. Must be in the same "
"space as `data`."),
default=None,
)
optional.add_argument("--prefix",
dest="prefix",
type=str,
help="Prefix for filenames generated.",
default="")
optional.add_argument(
"--convention",
dest="convention",
action="store",
choices=["orig", "bids"],
help=
("Filenaming convention. bids will use the latest BIDS derivatives version."
),
default="bids",
)
optional.add_argument(
"--fittype",
dest="fittype",
action="store",
choices=["loglin", "curvefit"],
help="Desired Fitting Method"
'"loglin" means that a linear model is fit'
" to the log of the data, default"
'"curvefit" means that a more computationally'
"demanding monoexponential model is fit"
"to the raw data",
default="loglin",
)
optional.add_argument(
"--fitmode",
dest="fitmode",
action="store",
choices=["all", "ts"],
help=("Monoexponential model fitting scheme. "
'"all" means that the model is fit, per voxel, '
"across all timepoints. "
'"ts" means that the model is fit, per voxel '
"and per timepoint."),
default="all",
)
optional.add_argument(
"--combmode",
dest="combmode",
action="store",
choices=["t2s", "paid"],
help=
("Combination scheme for TEs: t2s (Posse 1999, default), paid (Poser)"
),
default="t2s",
)
optional.add_argument(
"--n-threads",
dest="n_threads",
type=int,
action="store",
help=("Number of threads to use. Used by "
"threadpoolctl to set the parameter outside "
"of the workflow function. Higher numbers of "
"threads tend to slow down performance on "
"typical datasets. Default is 1."),
default=1,
)
optional.add_argument("--debug",
dest="debug",
help=argparse.SUPPRESS,
action="store_true",
default=False)
optional.add_argument("--quiet",
dest="quiet",
help=argparse.SUPPRESS,
action="store_true",
default=False)
parser._action_groups.append(optional)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
parser.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
parser.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
parser.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Binary mask of voxels to include in TE '
'Dependent ANAlysis. Must be in the same '
'space as `data`.'),
default=None)
parser.add_argument('--mix',
dest='mixm',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing mixing matrix. If not '
'provided, ME-PCA & ME-ICA is done.'),
default=None)
parser.add_argument('--ctab',
dest='ctab',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing a component table from which '
'to extract pre-computed classifications.'),
default=None)
parser.add_argument('--manacc',
dest='manacc',
help=('Comma separated list of manually '
'accepted components'),
default=None)
parser.add_argument('--kdaw',
dest='kdaw',
type=float,
help=('Dimensionality augmentation weight (Kappa). '
'Default=10. -1 for low-dimensional ICA'),
default=10.)
parser.add_argument('--rdaw',
dest='rdaw',
type=float,
help=('Dimensionality augmentation weight (Rho). '
'Default=1. -1 for low-dimensional ICA'),
default=1.)
parser.add_argument('--conv',
dest='conv',
type=float,
help='Convergence limit. Default 2.5e-5',
default='2.5e-5')
parser.add_argument('--sourceTEs',
dest='ste',
type=str,
help=('Source TEs for models. E.g., 0 for all, '
'-1 for opt. com., and 1,2 for just TEs 1 and '
'2. Default=-1.'),
default=-1)
parser.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s', 'ste'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default), ste (Poser)'),
default='t2s')
parser.add_argument('--cost',
dest='cost',
help=('Cost func. for ICA: '
'logcosh (default), cube, exp'),
choices=['logcosh', 'cube', 'exp'],
default='logcosh')
parser.add_argument('--denoiseTEs',
dest='dne',
action='store_true',
help='Denoise each TE dataset separately.',
default=False)
parser.add_argument('--strict',
dest='strict',
action='store_true',
help='Ignore low-variance ambiguous components',
default=False)
parser.add_argument('--no_gscontrol',
dest='gscontrol',
action='store_false',
help='Disable global signal regression.',
default=True)
parser.add_argument('--stabilize',
dest='stabilize',
action='store_true',
help=('Stabilize convergence by reducing '
'dimensionality, for low quality data'),
default=False)
parser.add_argument('--filecsdata',
dest='filecsdata',
help='Save component selection data',
action='store_true',
default=False)
parser.add_argument('--wvpca',
dest='wvpca',
help='Perform PCA on wavelet-transformed data',
action='store_true',
default=False)
parser.add_argument('--label',
dest='label',
type=str,
help='Label for output directory.',
default=None)
parser.add_argument(
'--seed',
dest='fixed_seed',
type=int,
help=('Value passed to repr(mdp.numx_rand.seed()) '
'Set to an integer value for reproducible ICA results; '
'otherwise, set to -1 for varying results across calls.'),
default=42)
parser.add_argument('--debug',
dest='debug',
help=argparse.SUPPRESS,
action='store_true',
default=False)
parser.add_argument('--quiet',
dest='quiet',
help=argparse.SUPPRESS,
action='store_true',
default=False)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
from ..info import __version__
verstr = 'tedana v{}'.format(__version__)
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
required.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
required.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
optional.add_argument('--out-dir',
dest='out_dir',
type=str,
metavar='PATH',
help='Output directory.',
default='.')
optional.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=("Binary mask of voxels to include in TE "
"Dependent ANAlysis. Must be in the same "
"space as `data`. If an explicit mask is not "
"provided, then Nilearn's compute_epi_mask "
"function will be used to derive a mask "
"from the first echo's data."),
default=None)
optional.add_argument('--fittype',
dest='fittype',
action='store',
choices=['loglin', 'curvefit'],
help=('Desired T2*/S0 fitting method. '
'"loglin" means that a linear model is fit '
'to the log of the data. '
'"curvefit" means that a more computationally '
'demanding monoexponential model is fit '
'to the raw data. '
'Default is "loglin".'),
default='loglin')
optional.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default)'),
default='t2s')
optional.add_argument(
'--tedpca',
dest='tedpca',
help=('Method with which to select components in TEDPCA. '
'PCA decomposition with the mdl, kic and aic options '
'is based on a Moving Average (stationary Gaussian) '
'process and are ordered from most to least aggresive. '
'Default=\'mdl\'.'),
choices=['kundu', 'kundu-stabilize', 'mdl', 'aic', 'kic'],
default='mdl')
optional.add_argument('--seed',
dest='fixed_seed',
metavar='INT',
type=int,
help=('Value used for random initialization of ICA '
'algorithm. Set to an integer value for '
'reproducible ICA results. Set to -1 for '
'varying results across ICA calls. '
'Default=42.'),
default=42)
optional.add_argument('--maxit',
dest='maxit',
metavar='INT',
type=int,
help=('Maximum number of iterations for ICA.'),
default=500)
optional.add_argument('--maxrestart',
dest='maxrestart',
metavar='INT',
type=int,
help=('Maximum number of attempts for ICA. If ICA '
'fails to converge, the fixed seed will be '
'updated and ICA will be run again. If '
'convergence is achieved before maxrestart '
'attempts, ICA will finish early.'),
default=10)
optional.add_argument('--tedort',
dest='tedort',
action='store_true',
help=('Orthogonalize rejected components w.r.t. '
'accepted components prior to denoising.'),
default=False)
optional.add_argument('--gscontrol',
dest='gscontrol',
required=False,
action='store',
nargs='+',
help=('Perform additional denoising to remove '
'spatially diffuse noise. Default is None. '
'This argument can be single value or a space '
'delimited list'),
choices=['t1c', 'gsr'],
default=None)
optional.add_argument(
'--no-png',
dest='no_png',
action='store_true',
help=('Creates a figures folder with static component '
'maps, timecourse plots and other diagnostic '
'images'),
default=False)
optional.add_argument('--png-cmap',
dest='png_cmap',
type=str,
help='Colormap for figures',
default='coolwarm')
optional.add_argument('--verbose',
dest='verbose',
action='store_true',
help='Generate intermediate and additional files.',
default=False)
optional.add_argument('--lowmem',
dest='low_mem',
action='store_true',
help=('Enables low-memory processing, including the '
'use of IncrementalPCA. May increase workflow '
'duration.'),
default=False)
optional.add_argument('--debug',
dest='debug',
action='store_true',
help=('Logs in the terminal will have increased '
'verbosity, and will also be written into '
'a .tsv file in the output directory.'),
default=False)
optional.add_argument('--quiet',
dest='quiet',
help=argparse.SUPPRESS,
action='store_true',
default=False)
optional.add_argument('-v', '--version', action='version', version=verstr)
parser._action_groups.append(optional)
rerungrp = parser.add_argument_group(
'arguments for rerunning the workflow')
rerungrp.add_argument('--t2smap',
dest='t2smap',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Precalculated T2* map in the same space as '
'the input data.'),
default=None)
rerungrp.add_argument('--mix',
dest='mixm',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing mixing matrix. If not '
'provided, ME-PCA & ME-ICA is done.'),
default=None)
rerungrp.add_argument('--ctab',
dest='ctab',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing a component table from which '
'to extract pre-computed classifications.'),
default=None)
rerungrp.add_argument('--manacc',
dest='manacc',
help=('Comma separated list of manually '
'accepted components'),
default=None)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
required.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
required.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
optional.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Binary mask of voxels to include in TE '
'Dependent ANAlysis. Must be in the same '
'space as `data`.'),
default=None)
optional.add_argument('--mix',
dest='mixm',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing mixing matrix. If not '
'provided, ME-PCA & ME-ICA is done.'),
default=None)
optional.add_argument('--ctab',
dest='ctab',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('File containing a component table from which '
'to extract pre-computed classifications.'),
default=None)
optional.add_argument('--manacc',
dest='manacc',
help=('Comma separated list of manually '
'accepted components'),
default=None)
optional.add_argument('--sourceTEs',
dest='ste',
type=str,
help=('Source TEs for models. E.g., 0 for all, '
'-1 for opt. com., and 1,2 for just TEs 1 and '
'2. Default=-1.'),
default=-1)
optional.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s', 'ste'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default), ste (Poser)'),
default='t2s')
optional.add_argument('--verbose',
dest='verbose',
action='store_true',
help='Generate intermediate and additional files.',
default=False)
optional.add_argument('--tedort',
dest='tedort',
action='store_true',
help=('Orthogonalize rejected components w.r.t. '
'accepted components prior to denoising.'),
default=False)
optional.add_argument('--gscontrol',
dest='gscontrol',
required=False,
action='store',
nargs='+',
help=('Perform additional denoising to remove '
'spatially diffuse noise. Default is None. '
'This argument can be single value or a space '
'delimited list'),
choices=['t1c', 'gsr'],
default=None)
optional.add_argument('--wvpca',
dest='wvpca',
help='Perform PCA on wavelet-transformed data',
action='store_true',
default=False)
optional.add_argument(
'--tedpca',
dest='tedpca',
help='Method with which to select components in TEDPCA',
choices=['mle', 'kundu', 'kundu-stabilize'],
default='mle')
optional.add_argument('--out-dir',
dest='out_dir',
type=str,
help='Output directory.',
default='.')
optional.add_argument(
'--seed',
dest='fixed_seed',
type=int,
help=('Value passed to repr(mdp.numx_rand.seed()) '
'Set to an integer value for reproducible ICA results; '
'otherwise, set to -1 for varying results across calls.'),
default=42)
optional.add_argument('--debug',
dest='debug',
help=argparse.SUPPRESS,
action='store_true',
default=False)
optional.add_argument('--quiet',
dest='quiet',
help=argparse.SUPPRESS,
action='store_true',
default=False)
parser._action_groups.append(optional)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
from ..info import __version__
verstr = "tedana v{}".format(__version__)
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group("Required Arguments")
required.add_argument(
"-d",
dest="data",
nargs="+",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=(
"Multi-echo dataset for analysis. May be a "
"single file with spatially concatenated data "
"or a set of echo-specific files, in the same "
"order as the TEs are listed in the -e "
"argument."
),
required=True,
)
required.add_argument(
"-e",
dest="tes",
nargs="+",
metavar="TE",
type=float,
help="Echo times (in ms). E.g., 15.0 39.0 63.0",
required=True,
)
optional.add_argument(
"--out-dir",
dest="out_dir",
type=str,
metavar="PATH",
help="Output directory.",
default=".",
)
optional.add_argument(
"--mask",
dest="mask",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=(
"Binary mask of voxels to include in TE "
"Dependent ANAlysis. Must be in the same "
"space as `data`. If an explicit mask is not "
"provided, then Nilearn's compute_epi_mask "
"function will be used to derive a mask "
"from the first echo's data."
),
default=None,
)
optional.add_argument(
"--prefix", dest="prefix", type=str, help="Prefix for filenames generated.", default=""
)
optional.add_argument(
"--convention",
dest="convention",
action="store",
choices=["orig", "bids"],
help=("Filenaming convention. bids will use the latest BIDS derivatives version."),
default="bids",
)
optional.add_argument(
"--fittype",
dest="fittype",
action="store",
choices=["loglin", "curvefit"],
help=(
"Desired T2*/S0 fitting method. "
'"loglin" means that a linear model is fit '
"to the log of the data. "
'"curvefit" means that a more computationally '
"demanding monoexponential model is fit "
"to the raw data. "
'Default is "loglin".'
),
default="loglin",
)
optional.add_argument(
"--combmode",
dest="combmode",
action="store",
choices=["t2s"],
help=("Combination scheme for TEs: t2s (Posse 1999, default)"),
default="t2s",
)
optional.add_argument(
"--tedpca",
dest="tedpca",
type=check_tedpca_value,
help=(
"Method with which to select components in TEDPCA. "
"PCA decomposition with the mdl, kic and aic options "
"is based on a Moving Average (stationary Gaussian) "
"process and are ordered from most to least aggressive. "
"Users may also provide a float from 0 to 1, "
"in which case components will be selected based on the "
"cumulative variance explained. "
"Default='mdl'."
),
default="mdl",
)
optional.add_argument(
"--seed",
dest="fixed_seed",
metavar="INT",
type=int,
help=(
"Value used for random initialization of ICA "
"algorithm. Set to an integer value for "
"reproducible ICA results. Set to -1 for "
"varying results across ICA calls. "
"Default=42."
),
default=42,
)
optional.add_argument(
"--maxit",
dest="maxit",
metavar="INT",
type=int,
help=("Maximum number of iterations for ICA."),
default=500,
)
optional.add_argument(
"--maxrestart",
dest="maxrestart",
metavar="INT",
type=int,
help=(
"Maximum number of attempts for ICA. If ICA "
"fails to converge, the fixed seed will be "
"updated and ICA will be run again. If "
"convergence is achieved before maxrestart "
"attempts, ICA will finish early."
),
default=10,
)
optional.add_argument(
"--tedort",
dest="tedort",
action="store_true",
help=("Orthogonalize rejected components w.r.t. accepted components prior to denoising."),
default=False,
)
optional.add_argument(
"--gscontrol",
dest="gscontrol",
required=False,
action="store",
nargs="+",
help=(
"Perform additional denoising to remove "
"spatially diffuse noise. Default is None. "
"This argument can be single value or a space "
"delimited list"
),
choices=["mir", "gsr"],
default=None,
)
optional.add_argument(
"--no-reports",
dest="no_reports",
action="store_true",
help=(
"Creates a figures folder with static component "
"maps, timecourse plots and other diagnostic "
"images and displays these in an interactive "
"reporting framework"
),
default=False,
)
optional.add_argument(
"--png-cmap", dest="png_cmap", type=str, help="Colormap for figures", default="coolwarm"
)
optional.add_argument(
"--verbose",
dest="verbose",
action="store_true",
help="Generate intermediate and additional files.",
default=False,
)
optional.add_argument(
"--lowmem",
dest="low_mem",
action="store_true",
help=(
"Enables low-memory processing, including the "
"use of IncrementalPCA. May increase workflow "
"duration."
),
default=False,
)
optional.add_argument(
"--n-threads",
dest="n_threads",
type=int,
action="store",
help=(
"Number of threads to use. Used by "
"threadpoolctl to set the parameter outside "
"of the workflow function. Higher numbers of "
"threads tend to slow down performance on "
"typical datasets. Default is 1."
),
default=1,
)
optional.add_argument(
"--debug",
dest="debug",
action="store_true",
help=(
"Logs in the terminal will have increased "
"verbosity, and will also be written into "
"a .tsv file in the output directory."
),
default=False,
)
optional.add_argument(
"--quiet", dest="quiet", help=argparse.SUPPRESS, action="store_true", default=False
)
optional.add_argument("-v", "--version", action="version", version=verstr)
parser._action_groups.append(optional)
rerungrp = parser.add_argument_group("Arguments for Rerunning the Workflow")
rerungrp.add_argument(
"--t2smap",
dest="t2smap",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=("Precalculated T2* map in the same space as the input data."),
default=None,
)
rerungrp.add_argument(
"--mix",
dest="mixm",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=("File containing mixing matrix. If not provided, ME-PCA & ME-ICA is done."),
default=None,
)
rerungrp.add_argument(
"--ctab",
dest="ctab",
metavar="FILE",
type=lambda x: is_valid_file(parser, x),
help=(
"File containing a component table from which "
"to extract pre-computed classifications. "
"Requires --mix."
),
default=None,
)
rerungrp.add_argument(
"--manacc",
dest="manacc",
metavar="INT",
type=int,
nargs="+",
help=("List of manually accepted components. Requires --ctab and --mix."),
default=None,
)
return parser
def _get_parser():
"""
Parses command line inputs for tedana
Returns
-------
parser.parse_args() : argparse dict
"""
parser = argparse.ArgumentParser()
# Argument parser follow templtate provided by RalphyZ
# https://stackoverflow.com/a/43456577
optional = parser._action_groups.pop()
required = parser.add_argument_group('required arguments')
required.add_argument('-d',
dest='data',
nargs='+',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Multi-echo dataset for analysis. May be a '
'single file with spatially concatenated data '
'or a set of echo-specific files, in the same '
'order as the TEs are listed in the -e '
'argument.'),
required=True)
required.add_argument('-e',
dest='tes',
nargs='+',
metavar='TE',
type=float,
help='Echo times (in ms). E.g., 15.0 39.0 63.0',
required=True)
optional.add_argument('--mask',
dest='mask',
metavar='FILE',
type=lambda x: is_valid_file(parser, x),
help=('Binary mask of voxels to include in TE '
'Dependent ANAlysis. Must be in the same '
'space as `data`.'),
default=None)
optional.add_argument(
'--fitmode',
dest='fitmode',
action='store',
choices=['all', 'ts'],
help=('Monoexponential model fitting scheme. '
'"all" means that the model is fit, per voxel, '
'across all timepoints. '
'"ts" means that the model is fit, per voxel '
'and per timepoint.'),
default='all')
optional.add_argument('--combmode',
dest='combmode',
action='store',
choices=['t2s', 'paid'],
help=('Combination scheme for TEs: '
't2s (Posse 1999, default), paid (Poser)'),
default='t2s')
optional.add_argument('--label',
dest='label',
type=str,
help='Label for output directory.',
default=None)
optional.add_argument('--debug',
dest='debug',
help=argparse.SUPPRESS,
action='store_true',
default=False)
optional.add_argument('--quiet',
dest='quiet',
help=argparse.SUPPRESS,
action='store_true',
default=False)
parser._action_groups.append(optional)
return parser
